Narrowing the Search for Diverse Talent

Talk to consultants, in-house human resources executives, and industry trend watchers, and you’re apt to hear about the ongoing battle for right-fit talent. It’s a complex issue, but it boils down to a single (if sweeping) question: How does the enterprise get the right candidates with the right skills into the pipeline and signed on the dotted line in alignment with organizational needs?

But describing the battle for talent simply in terms of skills is a two-dimensional discussion about a three-dimensional challenge. Talent isn’t just about the skills that candidates bring to the organization; it’s about the backgrounds, life experiences and other factors that make them complete employees – and valuable contributors.

The search for right-fit talent is the search for diverse talent. And increasingly, employers are turning to technology to help them identify and recruit from a more-diverse pool of candidates.

Sourcing diverse talent

Successfully recruiting diverse candidates can be challenging. Traditional sourcing methods are generally effective for increasing the applicant pool, but not necessarily increasing diversity of that pool. As organizations seek to infuse minorities, veterans, women, and other diverse talent into the workforce, the staggering amount of social data available may prove a treasure trove of insight into helping employers identify and find diverse talent.

To source diverse talent, recruiters are including Boolean strings for searching, looking at diversity association memberships and other criteria. For example, on LinkedIn there are more than 8,500 groups specifically for women and more than 350 for African Americans. Similarly, Facebook boasts more than 500 groups for women and an equal amount targeted to African Americans. And research shows that a higher percentage of Hispanic-Americans use social media than the population at large.

Using advanced search Boolean syntax will help to better narrow search results, or alternatively, using the Google advanced search dialog. The same rules apply to searches in LinkedIn. Include in the advanced search professional qualifying keywords and phrases along with “member list,” “member directory,” “member search,” and “is a member.” Lastly, add in diversity keyword phrases in search strings such as:

-People of Color

-African-American (with and without the hyphen)

-Asian-American (with and without the hyphen)

-Hispanic, Latino

-Native American

-Pacific Islander

-Other ethnicities




-Working mothers


-Gay and Lesbian, LGBT


In addition to searching around diversity key phrases, searching female names, all female university and college names, and names of associations and organizations can provide even more narrowed results.

Sample search strings to include:

(“project manager” AND “SaaS”) AND (Hispanic OR Latino OR “national society of Hispanic MBAs)

(“software developer” OR “programmer”) AND (Java OR Ruby OR rails) AND (“association of women in computing” OR “women in technology international”) –job

Using big data to identify, rank and predict the next hire

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Tapping into social streams for insights – particularly when seeking out top-tier talent before they may announce an intention to seek new work – is a growing trend. But this influx of social data can also be used to improve diversity sourcing efforts. In addition to social profiles, candidates’ participation in review and question and answer sites (LinkedIn Groups, Quora, Stack Overflow), niche online communities (Github, Meetup, DZone), and other activities can help flesh out a portrait that goes beyond a hard-skills inventory and includes a picture of candidate diversity.

But combing through large volumes of data by hand is beyond difficult; in a world where YouTube users upload 100 hours of new video content to the service every minute of every day, there’s simply no way to analyze social-stream data without strong technological support.

Looking beyond today’s social-stream data slicing and dicing, it’s clear that there are other mature technologies that could also aid in diversity sourcing. For example, facial recognition software has long been used in law enforcement, government and security. But its use is now bleeding into online communities and platforms as well.

Facebook uses face recognition for each of its 900 million users. Google and Apple both provide face recognition capabilities in their photo editing systems.  But while facial and skin recognition systems are more widely used than just a few years ago, they’re not yet ready for HR usage.

Recognizing facial features, locations or other factors – essentially, sorting candidates visually – may never become commonplace. But existing technology stacks that identify, rank and predict the behavior of candidates through their social streams and other online points of presence are already reality. It’s a proven technology that works, and by adapting search criteria, can be used to effectively screen for a pool of talent that is not only highly skilled, but highly diverse as well.

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